CN115604765A - Computing offload optimization method and device, electronic equipment and storage medium - Google Patents
Computing offload optimization method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN115604765A CN115604765A CN202211026689.6A CN202211026689A CN115604765A CN 115604765 A CN115604765 A CN 115604765A CN 202211026689 A CN202211026689 A CN 202211026689A CN 115604765 A CN115604765 A CN 115604765A
- Authority
- CN
- China
- Prior art keywords
- time delay
- target
- edge server
- internet
- block chain
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 104
- 238000005457 optimization Methods 0.000 title claims abstract description 53
- 238000004364 calculation method Methods 0.000 claims abstract description 89
- 238000005265 energy consumption Methods 0.000 claims abstract description 47
- 230000005540 biological transmission Effects 0.000 claims description 25
- 238000004891 communication Methods 0.000 claims description 17
- 238000012795 verification Methods 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 10
- 230000010365 information processing Effects 0.000 claims description 8
- 238000012549 training Methods 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 abstract description 11
- 238000012545 processing Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 230000002035 prolonged effect Effects 0.000 description 4
- 239000002245 particle Substances 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000001186 cumulative effect Effects 0.000 description 2
- 230000001934 delay Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000003190 augmentative effect Effects 0.000 description 1
- FFBHFFJDDLITSX-UHFFFAOYSA-N benzyl N-[2-hydroxy-4-(3-oxomorpholin-4-yl)phenyl]carbamate Chemical compound OC1=C(NC(=O)OCC2=CC=CC=C2)C=CC(=C1)N1CCOCC1=O FFBHFFJDDLITSX-UHFFFAOYSA-N 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/08—Load balancing or load distribution
- H04W28/09—Management thereof
- H04W28/0958—Management thereof based on metrics or performance parameters
- H04W28/0967—Quality of Service [QoS] parameters
- H04W28/0975—Quality of Service [QoS] parameters for reducing delays
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/08—Load balancing or load distribution
- H04W28/09—Management thereof
- H04W28/0925—Management thereof using policies
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Quality & Reliability (AREA)
- Computer And Data Communications (AREA)
Abstract
The application provides a computing offload optimization method and device, an electronic device and a storage medium, wherein the method comprises the following steps: establishing a cloud native network system based on a block chain; determining the credit degree of each pair of alliance nodes according to the edge calculation result of the target edge server and the task arrival rate of the target Internet of things equipment; generating a block chain consensus mechanism according to the credibility; acquiring a first time delay and a first consumed energy consumption required by the alliance chain system in the process of calculating unloading, and acquiring a second time delay required by the block chain consensus mechanism in the consensus process; and establishing a time delay minimization target model according to the first time delay, the first energy consumption and the second time delay, and obtaining a calculation unloading optimization scheme based on the time delay minimization target model. Through the method and the device, the problem that the computing real-time performance and data security requirements of the Internet of things equipment cannot be met simultaneously in the related technology is solved.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for computing offload optimization, an electronic device, and a storage medium.
Background
In recent years, the explosive growth of internet of things devices has prompted the continuous emergence of computing-intensive applications, such as: intelligent driving, virtual/augmented reality, online games and the like, and the application has the characteristics of large calculation amount, high real-time performance and the like. However, the computing and storage resources of the internet of things equipment are limited, and the service requirement of ultra-low time delay of computing-intensive application cannot be met. By utilizing a cloud native network architecture and taking the cloud as a center of the network, all services are directly connected with the cloud, so that the network is evolved from traditional end-to-end transmission connection into efficient cooperation of the cloud and the end. The edge cloud provides computing and storage resources, and the Internet of things equipment can unload computing tasks to the edge cloud server for execution, so that the computing time delay of the tasks and the energy consumption of the Internet of things equipment are effectively reduced. However, since the edge device is usually deployed at the edge of a network such as a wireless base station, has the characteristics of distribution and heterogeneity, and is in an open and insecure environment, the computation offload of the internet of things device faces a data security problem.
In the related technology, the data security of the Internet of things equipment in the task unloading process is ensured by introducing a block chain technology, and the block chain is a multi-party co-building, sharing and co-managing technology, so that the security and the reliability of the data in the calculation unloading process can be ensured through a consensus mechanism. However, the traditional block chain consensus mechanism has the problems of low transaction throughput, high resource consumption, prolonged transaction authentication and the like, and cannot ensure data security when the internet of things equipment with mass resource limitation frequently executes calculation unloading. In addition, most of the optimization targets of the existing calculation unloading method only consider reducing the time delay generated by the calculation unloading task, but do not consider the transaction authentication time delay of the block chain system.
Therefore, the prior art has the problem that the requirements of the internet of things equipment on calculation real-time performance and data security cannot be met at the same time.
Disclosure of Invention
The application provides a calculation unloading optimization method and device, electronic equipment and a storage medium, and aims to at least solve the problem that the real-time performance and data security of the internet of things equipment can not be met simultaneously in the related technology.
According to an aspect of an embodiment of the present application, there is provided a method for computing offload optimization, the method including:
establishing a block chain-based cloud native network system, wherein the cloud native network system comprises a alliance chain system for information processing in a computing unloading process, the alliance chain system comprises a plurality of pairs of alliance nodes, each pair of alliance nodes comprises target Internet of things equipment and a target edge server, the target Internet of things equipment is an object for sending task unloading information, and the target edge server is an object for computing the task unloading information and performing block chain consensus;
determining the credit degree of each pair of alliance nodes according to the edge calculation result of the target edge server and the task arrival rate of the target Internet of things equipment;
generating a block chain consensus mechanism according to the credibility;
acquiring a first time delay and a first consumed energy consumption required by the alliance chain system in a calculation unloading process, and acquiring a second time delay required by the block chain consensus mechanism in a consensus process;
and establishing a time delay minimization target model according to the first time delay, the first energy consumption and the second time delay, and obtaining a calculation unloading optimization scheme based on the time delay minimization target model.
According to another aspect of the embodiments of the present application, there is also provided a computing offload optimization apparatus, including:
the system comprises an establishing module and a processing module, wherein the establishing module is used for establishing a cloud native network system based on a block chain, the cloud native network system comprises a federation chain system for information processing in a calculation unloading process, the federation chain system comprises a plurality of pairs of federation nodes, each pair of federation nodes comprises target Internet of things equipment and a target edge server, the target Internet of things equipment is an object for sending task unloading information, and the target edge server is an object for calculating the task unloading information and performing block chain consensus;
the determining module is used for determining the credit degree of each pair of the alliance nodes according to the edge calculation result of the target edge server and the task arrival rate of the target Internet of things equipment;
the generating module is used for generating a block chain consensus mechanism according to the credibility;
the acquisition module is used for acquiring a first time delay and a first consumed energy which are required by the alliance chain system in the process of calculating and unloading, and acquiring a second time delay required by the block chain consensus mechanism in the consensus process;
and the obtaining module is used for establishing a time delay minimization target model according to the first time delay, the first energy consumption and the second time delay and obtaining a calculation unloading optimization scheme based on the time delay minimization target model.
Optionally, the generating module includes:
the creating unit is used for creating an initial unit by utilizing the target edge server, wherein the initial unit is used for storing the edge calculation result and the credibility of the alliance node;
the selection unit is used for selecting a first preset number of edge blocks by using the target edge server through a first preset method, and storing the hash values of the first preset number of edge blocks into the initial unit to obtain a first unit;
the computing unit is used for storing a random number into the first unit by using the target edge server, computing the hash value of the first unit at the moment and storing the hash value into the first unit to obtain a second unit;
a broadcasting unit for broadcasting the second unit to other edge servers using the target edge server;
the verification unit is used for verifying whether the second unit is legal or not by using the other edge servers, and if the second unit is legal, the second unit becomes a new edge block;
and the judging unit is used for verifying the new edge block by using the edge blocks generated by the other edge servers, judging whether the verification times reach an authentication threshold value by using the other edge servers, and if so, successfully identifying the data of the second unit by the alliance chain system.
Optionally, the obtaining module includes:
a first obtaining unit, configured to obtain a transmission delay of the task offloading information in a transmission process, the first energy consumption, a queuing delay of the task offloading information in a queue at an edge server, and an execution delay required for the edge server to execute the task offloading information;
a first obtaining unit, configured to obtain the first time delay according to the transmission time delay, the queuing time delay, and the execution time delay;
and the second acquisition unit is used for acquiring the second time delay of the task unloading information in the consensus process.
Optionally, the obtaining module includes:
a generating unit, configured to generate constraints for the first latency, the first energy consumption, and the second latency;
and the first establishing unit is used for establishing the time delay minimization target model based on the constraint condition.
The conversion unit is used for converting the time delay minimization target model into a second preset number of local models by a second preset method;
the second obtaining unit is used for optimizing the local models in parallel by training the parameters of the second preset number of local models to obtain corresponding local variables;
and the third obtaining unit is used for combining the local variables and optimizing the global variable of the time delay minimization target model to obtain the calculation unloading optimization scheme.
Optionally, the determining module includes:
a fourth obtaining unit, configured to obtain, by using the target edge server, a comprehensive reputation of the target edge server according to reputation of a third preset number of target internet-of-things devices to the target edge server;
a fifth obtaining unit, configured to obtain, by using the target edge server, a reputation of the target internet of things device according to a task arrival rate of the target internet of things device;
and the determining unit is used for determining the credit degree of each pair of the alliance nodes according to the comprehensive credit degree of the target edge server and the credit degree of the target Internet of things equipment.
Optionally, the establishing module includes:
the second establishing unit is used for establishing a block chain energized cloud native network system, wherein the cloud native network system comprises a second preset number of Internet of things devices and a fourth preset number of edge servers;
and the third establishing unit is used for establishing the alliance chain system in the process of computing unloading based on the cloud native network system.
According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory communicate with each other through the communication bus; wherein the memory is used for storing the computer program; a processor for performing the method steps in any of the above embodiments by running the computer program stored on the memory.
According to a further aspect of the embodiments of the present application, there is also provided a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to perform the method steps of any of the above embodiments when the computer program is executed.
In the embodiment of the application, a block chain-based cloud native network system is established, wherein the cloud native network system comprises a alliance chain system for processing information in a computing unloading process, the alliance chain system comprises a plurality of pairs of alliance nodes, each pair of alliance nodes comprises target internet of things equipment and a target edge server, the target internet of things equipment is an object for sending task unloading information, and the target edge server is an object for computing the task unloading information and performing block chain consensus; determining the credit degree of each pair of alliance nodes according to the edge calculation result of the target edge server and the task arrival rate of the target Internet of things equipment; generating a block chain consensus mechanism according to the credibility; acquiring a first time delay and a first consumed energy consumption required by the alliance chain system in the process of calculating unloading, and acquiring a second time delay required by the block chain consensus mechanism in the consensus process; and establishing a time delay minimization target model according to the first time delay, the first energy consumption and the second time delay, and obtaining a calculation unloading optimization scheme based on the time delay minimization target model. According to the embodiment of the application, on one hand, the transaction authentication time delay is reduced and the block chain consensus safety is improved by establishing the cloud native network system based on the block chain and generating the consensus mechanism based on the credibility of the alliance nodes, on the other hand, the calculation unloading task and the block chain consensus task are optimized simultaneously, the minimization of the system time delay of the block chain enabled cloud native network is realized, and the problem that the requirements of the Internet of things equipment on calculation real-time performance and data safety cannot be met simultaneously in the related technology is solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a schematic flow chart diagram illustrating an alternative method for computing offload optimization according to an embodiment of the present application;
fig. 2 is a diagram of an alternative block chain enabled cloud native network system model according to an embodiment of the present application;
FIG. 3 is a block diagram of an alternative computing offload optimization apparatus according to an embodiment of the present application;
fig. 4 is a block diagram of an alternative electronic device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In a cloud native network, energy consumption and computing time delay are reduced by offloading computing tasks to an edge server by internet of things equipment with limited resources, and data security in the process can be ensured by a consensus mechanism through a block chain technology. However, the traditional block chain consensus mechanism has the problems of low transaction throughput, high resource consumption, prolonged transaction authentication and the like, and cannot ensure data security when the internet of things equipment with mass resource limitation frequently executes calculation unloading tasks. In addition, the edge server executes both the block chain consensus mechanism and the computation and unloading task to generate time delay, and if the two time delays are independently optimized, the requirements of the internet of things equipment on computation real-time performance and data security service quality cannot be met at the same time. In order to solve the above problem, according to an aspect of an embodiment of the present application, there is provided a method for computing offload optimization, as shown in fig. 1, a flow of the method may include the following steps:
step S101, a block chain-based cloud native network system is established, wherein the cloud native network system comprises a alliance chain system for information processing in a computing unloading process, the alliance chain system comprises a plurality of pairs of alliance nodes, each pair of alliance nodes comprises target Internet of things equipment and a target edge server, the target Internet of things equipment is an object for sending task unloading information, and the target edge server is an object for computing the task unloading information and performing block chain consensus.
Optionally, a block chain enabled cloud native network system is established, as shown in fig. 2, where multiple internet of things devices and multiple edge servers exist in the cloud native network. A federation chain system commonly maintained by two interest groups, namely an Internet of things device and an edge server, is constructed in the cloud native network system. In the alliance chain system, a plurality of alliance nodes are generated by a plurality of internet of things devices and a plurality of edge servers, and one internet of things device and one edge server selected by the internet of things device can be selected to serve as a pair of alliance nodes. The Internet of things equipment serves as a block chain user and sends calculation unloading task information, the edge server serves as a block chain consensus node except for executing calculation unloading tasks, relevant information such as edge calculation results is packaged into blocks, a consensus algorithm is executed to maintain a block chain account book, and the requirements of low energy consumption, low time delay and high reliability of the Internet of things equipment on service quality are met.
And S102, determining the credit degree of each pair of alliance nodes according to the edge calculation result of the target edge server and the task arrival rate of the target Internet of things equipment.
Alternatively, for a blockchain system based on Directed Acyclic Graph (DAG), the blockchain system performance may be degraded if the transaction arrival rate is low or unstable. The higher the task arrival rate, the more computation and offloading tasks the edge server needs to execute, the more user data information needs to be protected, and the higher the transaction arrival rate of the blockchain. The internet of things equipment evaluates the satisfaction degree of the edge server according to the edge calculation result of the edge server, the higher the satisfaction degree of the internet of things equipment is, the better the calculation task of the edge server is completed, the higher the reliability of the edge server is, and the higher the credit degree score of the internet of things equipment on the edge server is. The longer the online time of the Internet of things equipment is, the higher the activity is, and the higher the credit score of the edge server on the Internet of things equipment is.
And step S103, generating a block chain consensus mechanism according to the credibility.
Optionally, the DAG-based blockchain uses an asynchronous communication mechanism, allowing the blockchain to perform bifurcation verification, requiring that two existing blocks in the network must be randomly selected for verification after a new block enters the network. For the alliance node with high credibility, the higher the probability that the issued transaction is selected for verification, the faster the transaction verification speed and the shorter the transaction authentication delay. For the alliance nodes with low credibility, the probability of verifying the issued transaction is low, the transaction verification speed is low, and the transaction authentication time is prolonged.
Step S104, acquiring a first time delay and a first energy consumption required by the alliance chain system in the process of calculating unloading, and acquiring a second time delay required by the block chain consensus mechanism in the consensus process.
Alternatively, assume that each task d i Respectively, the average data size and the maximum allowable time delay of i And
the system time delay in the whole calculation process comprises a first time delay, namely task unloading time delay
And a second time delay, namely a block chain transaction authentication time delay
Task offload latency includes task transmission latency
Queuing delay
And computation time delay of edge server
Since the data size of the edge calculation result is very small compared to the data size of the calculation task, the delay of the transmission of the calculation result is not considered. In addition, because the energy of the equipment of the Internet of things is limited, and the edge server has a power supply for continuously supplying power, the energy consumption of the equipment of the Internet of things is only considered, and the energy consumption generated by the edge server is not considered, so that the first energy consumption is equal to the energy consumption in the transmission stage
And S105, establishing a time delay minimization target model according to the first time delay, the first energy consumption and the second time delay, and obtaining a calculation unloading optimization scheme based on the time delay minimization target model.
Optionally, according to the characteristics of the DAG block chain consensus mechanism, the task arrival rate is lambda i The higher the blockchain transaction authentication latency
The lower the consensus security, but at this time the task offload latency is higher
The method is high and cannot meet the service quality requirement of the time delay of the Internet of things equipment. In order to meet the service quality requirements of the computing instantaneity and the data security of the equipment of the Internet of things at the same time, the computing unloading task and the block consensus task are optimized at the same time to minimize
And
the weighted sum of the time delay and the time delay is an optimization target, a mathematical optimization model is established as a time delay minimization target model, and a calculation unloading optimization scheme is obtained according to the mathematical optimization model.
In the embodiment of the application, a block chain-based cloud native network system is established, wherein the cloud native network system comprises a alliance chain system for processing information in a computing unloading process, the alliance chain system comprises a plurality of pairs of alliance nodes, each pair of alliance nodes comprises target internet of things equipment and a target edge server, the target internet of things equipment is an object for sending task unloading information, and the target edge server is an object for computing the task unloading information and performing block chain consensus; determining the credit degree of each pair of alliance nodes according to the edge calculation result of the target edge server and the task arrival rate of the target Internet of things equipment; generating a block chain consensus mechanism according to the credibility; acquiring a first time delay and first energy consumption required by a alliance chain system in a calculation unloading process and acquiring a second time delay required by a block chain consensus mechanism in a consensus process; and establishing a time delay minimization target model according to the first time delay, the first energy consumption and the second time delay, and obtaining a calculation unloading optimization scheme based on the time delay minimization target model. According to the embodiment of the application, on one hand, the transaction authentication time delay is reduced and the block chain consensus safety is improved by establishing the cloud native network system based on the block chain and generating the consensus mechanism based on the credibility of the alliance nodes, on the other hand, the calculation unloading task and the block chain consensus task are optimized simultaneously, the minimization of the system time delay of the block chain enabled cloud native network is realized, and the problem that the requirements of the Internet of things equipment on calculation real-time performance and data safety cannot be met simultaneously in the related technology is solved.
As an alternative embodiment, the mechanism for generating block chain consensus according to reputation comprises:
creating an initial unit by using a target edge server, wherein the initial unit is used for storing an edge calculation result and the credibility of the alliance node;
selecting a first preset number of edge blocks by using a target edge server through a first preset method, and storing the hash values of the first preset number of edge blocks into an initial unit to obtain a first unit;
storing a random number into a first unit by using a target edge server, calculating a hash value of the first unit at the moment, and storing the hash value into the first unit to obtain a second unit;
broadcasting the second unit to other edge servers using the target edge server;
verifying whether the second unit is legal by using other edge servers, and if the second unit is legal, the second unit becomes a new edge block;
and verifying the new edge block by using the edge blocks generated by other edge servers, judging whether the verification times reach an authentication threshold value or not by using other target edge servers, and if so, successfully identifying the data of the second unit by the cloud native network system.
Optionally, the edge server m performs a calculation offloading task of the internet of things device i, and takes the result of the calculation task as a transaction on the blockchain. At this time, the transaction arrival rate of the internet of things device i is lambda i The edge server m records a transaction arrival rate of
ρ i,m To compute the task offload policy, N is a positive integer. The edge server m creates a Unit (Unit) for each transaction, which is used to store transaction information and the reputation of the federation nodes and digitally sign the transaction with a private key.
The entanglement (Tangle) is based on the most representative consensus method in the DAG block chain, in the entanglement consensus process, each Unit needs to verify two existing edge blocks (Tips) before adding a DAG block chain account book, the first preset number may be two or other values, and the embodiment of the application does not limit specific values. When a plurality of edge blocks (more than two) exist, the Tangle selects two edge blocks by adopting a Monte Carlo Chain (MCMC) edge block selection algorithm based on credibility, and in the process of selecting the two edge blocks, the edge server generates a plurality of wandering particles and makes the wandering particles have probability
Each independently wandering in the direction of the edge zone, wherein,
and
representing the cumulative weight values of x, y and z, respectively.
And
representing reputation values for nodes publishing x, y and z, respectively. z → x indicates that z refers directly to x, and that α and β are non-negative tunable parameters. The edge block where the two particles that arrive at the edge block first stay is the selected block. The higher the credit degree of the alliance node is, the higher the probability that the edge block issued by the alliance node is selected for verification is, the higher the transaction verification speed is, and the shorter the transaction authentication time delay is. When a newly arrived Unit selects two edge blocks, it needs to check whether the two edge blocks conflict. If there is no conflict, the hash values of the two edge blocks are stored into the Unit.
In order to prevent DDoS (Distributed Denial of Service) attacks and avoid the waste of computation power, before issuing the transaction, the edge server needs to make a light-weighted Proof of Work (PoW), i.e., package the content of the transaction and the hash value of the selected two edge blocks, add a random number Nonce, and compute the hash value. And stores this hash value in the Unit, which is then broadcast to other edge servers in the network.
And after receiving the Unit, other edge servers verify whether the Unit is legal or not according to the digital signature and the Nonce value. If it is legal, this Unit is added to its local DAG ledger, at which time this legal Unit becomes an edge chunk.
The steps of generating edge blocks by other edge servers are the same as the above steps, and the edge blocks generated by other edge servers can directly or indirectly verify the Unit edge block with a probability. In a DAG blockchain, each block has an accumulated weight value (initial value of 1) that is incremented by 1 each time the block is verified, either directly or indirectly. When the accumulated weight value of the block reaches the authentication threshold value, the transaction in the block is confirmed as a successful transaction through the whole network.
In the embodiment of the application, by designing a consensus mechanism of a DAG block chain based on credit, the problems that the traditional DAG consensus mechanism is prolonged in transaction authentication, is easily attacked by malicious nodes and is insufficient in consensus security are solved. The transaction authentication time delay is effectively reduced, the block consensus security is improved, and the data security requirement of the Internet of things equipment is met.
As an alternative embodiment, the obtaining a first delay and a first energy consumption consumed by the federation chain system in the process of calculating the uninstallation, and the obtaining a second delay required by the block chain consensus mechanism in the consensus process include:
acquiring transmission delay and first energy consumption of the task unloading information in a transmission process, queuing delay of the task unloading information in a queue of an edge server and execution delay required by the edge server to execute the task unloading information;
obtaining a first time delay according to the transmission time delay, the queuing time delay and the execution time delay;
and acquiring a second time delay of the task unloading information in the consensus process.
Optionally, in a cloud native network, the internet of things device offloads the computing task to the edge server through the wireless channel for execution, and in the process, transmission delay and first energy consumption, that is, transmission energy consumption, may be generated. Internet of things equipment d i Is offloaded to the edge server S m The uplink data transmission rate and transmission delay of (d) may be expressed as:
wherein, B is channel bandwidth, orthogonal Frequency Division Multiplexing (OFDM) is adopted, and bandwidth of each subcarrier is B 0 ＝BN，N m To offload a computing task to S m The number of terminal devices. p is a radical of i,m And H i,m Are respectively d i To S m The transmission power and the channel gain of the mobile station,
interference power, sigma, generated for other internet of things devices 2 Is the background noise power. Internet of things equipment d in transmission stage i With an energy consumption of
Because the energy of the equipment of the Internet of things is limited, and the edge server has a power supply for continuously supplying power, the energy consumption of the equipment of the Internet of things is only considered, and the energy consumption generated by the edge server is not considered.
The edge server has limited computing resources, cannot simultaneously execute computing and offloading tasks of multiple internet of things devices, and can generate queuing delay when multiple computing and offloading tasks exist. The model of the unloading task waiting to be executed in the edge server processing task buffer area is expressed as a D/M/1 queue by adopting a queuing theory according to the FCFS service rule of the equipment task of the Internet of things, S m The task arrival rate of
d i At S m The average queuing time of each of the above computing tasks is:
wherein,
very small absolute value, mu m Is S m Service rate of λ m <μ m 。
Each computing task d i At S m The calculated delay of (c) can be expressed as:
wherein, C i Representing the number of CPU cycles required to process 1 bit of input data, f m Denotes S m Of CPUThe period frequency.
The sum of the transmission delay, the queuing delay and the calculation delay is a first delay, namely the task unloading delay.
Under the condition of non-saturation of a wireless transmission environment, after the edge server finishes the calculation unloading task of the Internet of things equipment, each calculation result and related information are packaged into blocks, block chain consensus is carried out, and then the data safety of calculation unloading is protected. For blockchain networks, the transaction arrival rate is related to the service rate of the edge server, and the transaction arrival rate follows an average value of
The poisson process of (a). At S m The cumulative weight growth rate of the block generated above is
When the accumulated weight value of the block reaches the authentication weight threshold W, the consumed block chain transaction authentication delay, i.e. the second delay, may be represented as:
in the embodiment of the application, the task unloading delay is obtained by calculating the task transmission delay, the queuing delay and the calculation delay, the first energy consumption and the block chain transaction authentication delay are obtained by calculating, all delays in the whole task unloading process and the block chain transaction authentication process are comprehensively considered, the weighted sum of the task unloading delay and the block chain transaction authentication delay is taken as an optimization target, a mathematical optimization model is established, and the calculation real-time requirement of the internet of things equipment is met.
As an alternative embodiment, the establishing the delay minimization target model according to the first delay, the first energy consumption and the second delay comprises:
generating constraints for the first time delay, the first energy consumption and the second time delay;
and establishing a time delay minimization target model based on the constraint conditions.
Optionally, in the embodiment of the present application, joint optimization is performed on the task unloading delay and the block chain consensus mechanism of the internet of things device, and the generated constraint condition and the established mathematical optimization model are as follows:
wherein, ω is 1 And ω 2 For the weighting coefficients, C1 and C2 are respectively constraint conditions for time delay and energy consumption that can be tolerated by the internet of things device. C3 is guarantee S m The upper task arrival rate does not exceed its service rate. C4 and C5 are constraints on the computation offload decision, and it is ensured that each edge server can serve multiple internet of things devices, but each internet of things device can only offload computation tasks to one edge server for execution. C6 is a constraint on the number of internet of things devices served by each edge server.
In the embodiment of the application, constraint conditions are generated aiming at time delay and energy consumption in the process of calculating the task of unloading the Internet of things equipment. And then, aiming at minimizing task unloading delay and block chain transaction authentication delay, a mathematical optimization model is established based on the constraint conditions, and a calculation unloading optimization scheme can be obtained through the mathematical optimization model, so that the system delay minimization of the cloud native network with block chain energizing is realized, and the problem that the calculation real-time requirement of the Internet of things equipment cannot be met in the related technology is solved.
As an alternative embodiment, obtaining the calculation offloading optimization scheme based on the time delay minimization objective model includes:
converting the time delay minimization target model into a second preset number of local models by a second preset method;
optimizing the local models in parallel by training parameters of a second preset number of local models to obtain corresponding local variables;
and combining the local variables, and optimizing the global variable of the time delay minimized target model to obtain a calculation unloading optimization scheme.
Alternatively, since the computational offload optimization problem is not a convex problem, and as the number of users in the cloud native network increases, the size and complexity of the problem will increase rapidly. According to the method, an original complex calculation unloading optimization problem is converted into a second preset number of unconstrained subproblems to be solved by adopting an alternating direction multiplier method, the second preset number represents a plurality of problems, and the embodiment of the method is not limited to specific numerical values. Thereafter, the compute nodes optimize the sub-problem in parallel by training their own local model parameters. Then, all local variables are combined to optimize global variables. And finally, obtaining a global solution through iteration.
In the embodiment of the application, the calculation unloading task and the block consensus task are optimized simultaneously, the optimization problem is simplified by adopting an alternating direction multiplier method, and finally, a calculation unloading optimization scheme is obtained. According to the method and the device, optimization efficiency is improved, and the obtained calculation unloading optimization scheme can meet the service quality requirements of the internet of things equipment with limited mass resources on calculation real-time performance and data safety when the calculation unloading tasks are frequently executed. The problem that the real-time performance and the data safety of the Internet of things equipment on calculation cannot be met simultaneously in the related technology is solved.
As an optional embodiment, determining the reputation of each pair of federation nodes according to the edge calculation result of the target edge server and the task arrival rate of the target internet of things device includes:
obtaining the comprehensive credit degree of the target edge server by using the target edge server according to the credit degrees of the target internet of things equipment with a third preset number to the target edge server;
obtaining the credit degree of the target Internet of things equipment by using the target edge server according to the task arrival rate of the target Internet of things equipment;
and determining the credit degree of each pair of alliance nodes according to the comprehensive credit degree of the target edge server and the credit degree of the target Internet of things equipment.
Optionally, since the edge server m can serve multiple internet of things devices at the same time, the reputation thereof should be generated by scoring the comprehensive reputation of the multiple internet of things devices, where the comprehensive reputation is
Where ρ is i,m To compute task offload policies, r i→m After the Internet of things equipment i receives the calculation result of the edge server m, the credit degree of the edge server m is graded according to the satisfaction degree of the equipment i to the calculation result of the edge, N is a positive integer,
is a third predetermined number. Obtaining the comprehensive credit degree S i→m The edge server then stores the integrated reputation.
Because the task arrival rate of the internet of things equipment indirectly reflects the transaction arrival rate of the block chain, the edge server m according to the task arrival rate lambda of the internet of things equipment i Scoring S for credibility of Internet of things equipment m→i ＝g(λ i ) Are combined with each otherS m→i And storing.
Taking each Internet of things device and the selected edge server as a pair of alliance nodes, and obtaining the credibility S of each pair of alliance nodes after calculation i,m ＝αS i→m +βS m→i (9)
Alpha and beta are credit degree weight factors respectively, and the internet of things equipment can adjust the weight of the credit degree according to the service quality requirement.
In the embodiment of the application, each piece of internet-of-things equipment and the selected edge server are used as a pair of alliance nodes, the influence of the respective credit degrees on the block chain performance is considered, the credit degree of each pair of alliance nodes is calculated, the block consensus safety is improved, and the service quality requirement of the internet-of-things equipment on data safety is met.
As an alternative embodiment, the building block chain based cloud native network system includes:
establishing a block chain energized cloud native network system, wherein the cloud native network system comprises a second preset number of Internet of things devices and a fourth preset number of edge servers;
and establishing a union link system in the calculation unloading process based on the cloud native network system.
Optionally, a block chain enabled cloud native network system model is shown in fig. 2, and the system includes a cloud native network and a block chain network. There are N internet of things devices D = { D) in the cloud native network 1 ,...,d i ,...,d N And M edge servers
The second preset number is N, the fourth preset number is M, N and M represent a plurality, and the embodiment of the present application does not limit specific numerical values. Internet of things equipment d i When the computing-intensive application is executed, a series of same computing tasks are generated, and the Internet of things equipment directly transmits the computing tasks to the edge server through the wireless channel for execution. Assuming that within a time slot, d i The number of arriving tasks is n i ＝υ/θ i In which v is a time slotLength of time, θ i Is the time interval of arrival of the task, then λ i ＝1/θ i Is d i The task arrival rate of. The edge servers are connected by a wired link. The blockchain network is used to ensure the security of the computation offload data.
And constructing a alliance chain which is commonly maintained by two interest groups, namely the Internet of things equipment and the edge server. The Internet of things equipment serves as a blockchain user to send task unloading information, the edge server serves as a blockchain consensus node, and a calculation unloading task and a blockchain consensus task are executed. The work flow of the alliance chain is as follows: firstly, the Internet of things equipment sends task unloading information to the edge server, the edge server executes a calculation unloading task, and a calculation result is returned to the Internet of things equipment after the task unloading information is completed. And then, the edge server packs the calculation task result and related information into blocks, completes block chain consensus according to a block chain consensus mechanism based on DAG, and adds data information to the block chain after verification is correct, so that the user privacy and the data safety in the calculation unloading process are ensured.
In the embodiment of the application, a cloud native network system based on a block chain is established, then a alliance chain model of the internet of things equipment and the edge server in the computing unloading process is established according to the cloud native network system, and the node is prevented from being maliciously tampered and data information data of a forged user are prevented through a block chain consensus mechanism, so that the security requirement of the internet of things equipment is met.
According to another aspect of the embodiment of the present application, there is also provided a computation offload optimization apparatus for implementing the computation offload optimization method. Fig. 3 is a block diagram of an alternative computing offload optimization apparatus according to an embodiment of the present application, and as shown in fig. 3, the apparatus may include:
the establishing module 301 is configured to establish a block chain-based cloud native network system, where the cloud native network system includes a federation chain system for information processing in a computation offloading process, the federation chain system includes a plurality of pairs of federation nodes, each pair of federation nodes includes a target internet-of-things device and a target edge server, the target internet-of-things device is an object for sending task offloading information, and the target edge server is an object for computing task offloading information and performing block chain consensus;
a determining module 302, configured to determine a reputation of each pair of federation nodes according to an edge calculation result of the target edge server and a task arrival rate of the target internet of things device;
the generating module 303 is configured to generate a block chain consensus mechanism according to the reputation degree;
an obtaining module 304, configured to obtain a first time delay and a first energy consumption consumed by the alliance chain system in the computation offloading process, and obtain a second time delay required by the block chain consensus mechanism in the consensus process;
an obtaining module 305, configured to establish a delay minimization objective model according to the first delay, the first energy consumption, and the second delay, and obtain a calculation offloading optimization scheme based on the delay minimization objective model.
As an alternative embodiment, the generating module comprises:
the system comprises a creating unit, a calculating unit and a processing unit, wherein the creating unit is used for creating an initial unit by using a target edge server, and the initial unit is used for storing an edge calculation result and the credibility of a federation node;
the selection unit is used for selecting a first preset number of edge blocks by using the target edge server through a first preset method, and storing the hash values of the first preset number of edge blocks into the initial unit to obtain a first unit;
the calculating unit is used for storing a random number into the first unit by using the target edge server, calculating the hash value of the first unit at the moment and storing the hash value into the first unit to obtain a second unit;
a broadcasting unit for broadcasting the second unit to other edge servers using the target edge server;
the verification unit is used for verifying whether the second unit is legal by using other edge servers, and if the second unit is legal, the second unit becomes a new edge block;
and the judging unit is used for verifying the new edge block by using the edge blocks generated by other edge servers, judging whether the verification times reach an authentication threshold value or not by using other edge servers, and if so, successfully identifying the data of the second unit by the cloud native network system.
As an alternative embodiment, the obtaining module includes:
the first acquisition unit is used for acquiring transmission delay and first energy consumption of the task unloading information in the transmission process, queuing delay of the task unloading information in the edge server and execution delay required by the edge server to execute the task unloading information;
the first obtaining unit is used for obtaining a first time delay according to the transmission time delay, the queuing time delay and the execution time delay;
and the second acquisition unit is used for acquiring a second time delay of the task unloading information in the consensus process.
As an alternative embodiment, the obtaining module includes:
the generating unit is used for generating constraint conditions aiming at the first time delay, the first energy consumption and the second time delay;
and the first establishing unit is used for establishing the time delay minimization target model based on the constraint condition.
The conversion unit is used for converting the time delay minimization target model into a second preset number of local models by a second preset method;
the second obtaining unit is used for optimizing the local models in parallel by training parameters of a second preset number of local models to obtain corresponding local variables;
and the third obtaining unit is used for combining the local variables and optimizing the global variable of the time delay minimization target model to obtain a calculation unloading optimization scheme.
As an alternative embodiment, the determining module comprises:
the fourth obtaining unit is used for obtaining the comprehensive credibility of the target edge server by using the target edge server according to the credibility of the target edge server by the third preset number of target internet-of-things devices;
a fifth obtaining unit, configured to obtain a reputation of the target internet of things device according to the task arrival rate of the target internet of things device by using the target edge server;
and the determining unit is used for determining the credit degree of each pair of the alliance nodes according to the comprehensive credit degree of the target edge server and the credit degree of the target Internet of things equipment.
As an alternative embodiment, the establishing module includes:
the second establishing unit is used for establishing a block chain energized cloud native network system, wherein the cloud native network system comprises a fourth preset number of Internet of things devices and a fifth preset number of edge servers;
and the third establishing unit is used for establishing a alliance chain system in the process of computing unloading based on the cloud native network system.
It should be noted here that the modules described above are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above embodiments.
Fig. 4 is a block diagram of an alternative electronic device according to an embodiment of the present application, as shown in fig. 4, including a processor 401, a communication interface 402, a memory 403, and a communication bus 404, where the processor 401, the communication interface 402, and the memory 403 communicate with each other through the communication bus 404, where,
a memory 403 for storing a computer program;
the processor 401, when executing the computer program stored in the memory 403, implements the following steps:
establishing a cloud native network system based on a block chain, wherein the cloud native network system comprises a union chain system for information processing in a calculation unloading process, the union chain system comprises a plurality of pairs of union nodes, each pair of union nodes comprises target Internet of things equipment and a target edge server, the target Internet of things equipment is an object for sending task unloading information, and the target edge server is an object for calculating the task unloading information and performing block chain consensus;
determining the credit degree of each pair of alliance nodes according to the edge calculation result of the target edge server and the task arrival rate of the target Internet of things equipment;
generating a block chain consensus mechanism according to the credibility;
acquiring a first time delay and a first consumed energy consumption required by the alliance chain system in the process of calculating unloading, and acquiring a second time delay required by the block chain consensus mechanism in the consensus process;
and establishing a time delay minimization target model according to the first time delay, the first energy consumption and the second time delay, and obtaining a calculation unloading optimization scheme based on the time delay minimization target model.
Alternatively, in this embodiment, the communication bus may include, but is not limited to, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 4, but this does not indicate only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The memory may include RAM, and may also include, but is not limited to, non-volatile memory (non-volatile memory), such as at least one disk memory. Alternatively, the memory may be at least one memory device located remotely from the processor.
As an example, as shown in fig. 4, the memory 403 may include, but is not limited to, a setup module 301, a determination module 302, a generation module 303, an acquisition module 304, and an obtaining module 305 in the computation offloading optimization apparatus. In addition, other module units in the above calculation offloading optimization apparatus may also be included, but are not limited to this, and are not described in detail in this example.
The processor may be a general-purpose processor, and may include but is not limited to: a CPU (Central Processing Unit), an NP (Network Processor), and the like; but also a DSP (Digital Signal Processing), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment is not described herein again.
Those of ordinary skill in the art will appreciate that the configuration shown in FIG. 4 is merely illustrative. Fig. 4 does not limit the structure of the electronic device. For example, the terminal device may also include, but is not limited to, more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 4, or have a different configuration than shown in FIG. 4.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, and the program may be stored in a computer-readable storage medium, which may include, but is not limited to: flash disk, ROM, RAM, magnetic or optical disk, and the like.
According to still another aspect of an embodiment of the present application, there is also provided a storage medium. Optionally, in this embodiment, the storage medium may be configured to store a program code for executing the calculation uninstallation optimization method.
Optionally, in this embodiment, the storage medium may be located on at least one of a plurality of network devices in a network shown in the above embodiment.
Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps:
establishing a block chain-based cloud native network system, wherein the cloud native network system comprises a alliance chain system for information processing in a calculation unloading process, the alliance chain system comprises a plurality of pairs of alliance nodes, each pair of alliance nodes comprises target Internet of things equipment and a target edge server, the target Internet of things equipment is an object for sending task unloading information, and the target edge server is an object for calculating the task unloading information and performing block chain consensus;
determining the credit degree of each pair of alliance nodes according to the edge calculation result of the target edge server and the task arrival rate of the target Internet of things equipment;
generating a block chain consensus mechanism according to the credibility;
acquiring a first time delay and a first consumed energy consumption required by the alliance chain system in the process of calculating unloading, and acquiring a second time delay required by the block chain consensus mechanism in the consensus process;
and establishing a time delay minimization target model according to the first time delay, the first energy consumption and the second time delay, and obtaining a calculation unloading optimization scheme based on the time delay minimization target model.
Optionally, the specific example in this embodiment may refer to the example described in the above embodiment, which is not described again in this embodiment.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a U disk, a ROM, a RAM, a removable hard disk, a magnetic disk, or an optical disk.
In the description herein, reference to the description of the terms "this embodiment," "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. A method for computing offload optimization, the method comprising:
establishing a block chain-based cloud native network system, wherein the cloud native network system comprises a alliance chain system for information processing in a computing unloading process, the alliance chain system comprises a plurality of pairs of alliance nodes, each pair of alliance nodes comprises target Internet of things equipment and a target edge server, the target Internet of things equipment is an object for sending task unloading information, and the target edge server is an object for computing the task unloading information and performing block chain consensus;
determining the credit degree of each pair of alliance nodes according to the edge calculation result of the target edge server and the task arrival rate of the target Internet of things equipment;
generating a block chain consensus mechanism according to the credibility;
acquiring a first time delay and a first consumed energy consumption required by the alliance chain system in a calculation unloading process, and acquiring a second time delay required by the block chain consensus mechanism in a consensus process;
and establishing a time delay minimization target model according to the first time delay, the first energy consumption and the second time delay, and obtaining a calculation unloading optimization scheme based on the time delay minimization target model.
2. The method of claim 1, wherein the generating a block chain consensus mechanism according to the reputation comprises:
creating an initial unit by using the target edge server, wherein the initial unit is used for storing the edge calculation result and the credibility of the federation node;
selecting a first preset number of edge blocks by using the target edge server through a first preset method, and storing the hash values of the first preset number of edge blocks into the initial unit to obtain a first unit;
storing a random number into the first unit by using the target edge server, calculating a hash value of the first unit at the moment, and storing the hash value into the first unit to obtain a second unit;
broadcasting the second cell to other edge servers using the target edge server;
verifying whether the second unit is legal by using the other edge servers, and if so, enabling the second unit to become a new edge block;
and verifying the new edge block by using the edge blocks generated by the other edge servers, judging whether the verification times reach an authentication threshold value by using the other target edge servers, and if so, successfully identifying the data of the second unit by the alliance chain system.
3. The method of claim 1, wherein the obtaining a first latency and a first energy consumption required by the federation chain system in calculating an offloading process and obtaining a second latency required by the blockchain consensus mechanism in a consensus process comprises:
acquiring transmission delay of the task unloading information in a transmission process, the first energy consumption, queuing delay of the task unloading information in a queue at an edge server and execution delay required by the edge server to execute the task unloading information;
obtaining the first time delay according to the transmission time delay, the queuing time delay and the execution time delay;
and acquiring the second time delay of the task unloading information in the consensus process.
4. The method of claim 1, wherein the establishing a latency minimization objective model according to the first latency, the first energy consumption, and the second latency comprises:
generating constraints for the first latency, the first energy consumption, and the second latency;
and establishing the time delay minimization target model based on the constraint condition.
5. The method of claim 1, wherein deriving a computational offload optimization scheme based on the latency minimization objective model comprises:
converting the time delay minimization target model into a second preset number of local models by a second preset method;
optimizing the local models in parallel by training the parameters of the second preset number of local models to obtain corresponding local variables;
and combining the local variables, and optimizing the global variable of the time delay minimization target model to obtain the calculation unloading optimization scheme.
6. The method of claim 1, wherein the determining the reputation of each pair of federation nodes according to the edge calculation result of the target edge server and the task arrival rate of the target internet of things device comprises:
obtaining a comprehensive credibility of the target edge server by using the target edge server according to the credibility of a third preset number of target Internet of things equipment to the target edge server;
obtaining the credit degree of the target Internet of things equipment by using the target edge server according to the task arrival rate of the target Internet of things equipment;
and determining the credit degree of each pair of the alliance nodes according to the comprehensive credit degree of the target edge server and the credit degree of the target Internet of things equipment.
7. The method of claim 1, wherein establishing a block chain based cloud native network system comprises:
establishing a block chain energized cloud native network system, wherein the cloud native network system comprises a second preset number of Internet of things devices and a fourth preset number of edge servers;
and establishing the alliance chain system in the computing unloading process based on the cloud native network system.
8. A computing offload optimization apparatus, comprising:
the system comprises an establishing module, a block chain-based cloud native network system and a block chain identification module, wherein the cloud native network system comprises a alliance chain system for information processing in a computing unloading process, the alliance chain system comprises a plurality of pairs of alliance nodes, each pair of alliance nodes comprises target Internet of things equipment and a target edge server, the target Internet of things equipment is an object for sending task unloading information, and the target edge server is an object for computing the task unloading information and performing block chain consensus;
the determining module is used for determining the credit degree of each pair of the alliance nodes according to the edge calculation result of the target edge server and the task arrival rate of the target Internet of things equipment;
the generating module is used for generating a block chain consensus mechanism according to the credibility;
the acquisition module is used for acquiring a first time delay and a first consumed energy which are required by the alliance chain system in the process of calculating and unloading, and acquiring a second time delay required by the block chain consensus mechanism in the consensus process;
and the obtaining module is used for establishing a time delay minimization target model according to the first time delay, the first energy consumption and the second time delay and obtaining a calculation unloading optimization scheme based on the time delay minimization target model.
9. An electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein said processor, said communication interface and said memory communicate with each other via said communication bus,
the memory for storing a computer program;
the processor for performing the method steps of any one of claims 1 to 7 by running the computer program stored on the memory.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method steps of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211026689.6A CN115604765A (en) | 2022-08-25 | 2022-08-25 | Computing offload optimization method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211026689.6A CN115604765A (en) | 2022-08-25 | 2022-08-25 | Computing offload optimization method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115604765A true CN115604765A (en) | 2023-01-13 |
Family
ID=84842795
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211026689.6A Pending CN115604765A (en) | 2022-08-25 | 2022-08-25 | Computing offload optimization method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115604765A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117042051A (en) * | 2023-08-29 | 2023-11-10 | 燕山大学 | Task unloading strategy generation method, system, equipment and medium in Internet of vehicles |
-
2022
- 2022-08-25 CN CN202211026689.6A patent/CN115604765A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117042051A (en) * | 2023-08-29 | 2023-11-10 | 燕山大学 | Task unloading strategy generation method, system, equipment and medium in Internet of vehicles |
| CN117042051B (en) * | 2023-08-29 | 2024-03-08 | 燕山大学 | Task unloading strategy generation method, system, equipment and medium in Internet of vehicles |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Aujla et al. | BloCkEd: Blockchain-based secure data processing framework in edge envisioned V2X environment | |
| CN111800495B (en) | Task unloading method in vehicle fog calculation | |
| Asheralieva et al. | Reputation-based coalition formation for secure self-organized and scalable sharding in IoT blockchains with mobile-edge computing | |
| Conti et al. | Blockchain trilemma solver algorand has dilemma over undecidable messages | |
| US11641286B2 (en) | Sybil-resistant identity generation | |
| CN112527912B (en) | Data processing method and device based on block chain network and computer equipment | |
| Liu et al. | A decentralized cloud firewall framework with resources provisioning cost optimization | |
| CN112119620B (en) | System and method for propagating data packets in a network of nodes | |
| CN115499379B (en) | Information interaction method, device, equipment and medium based on block chain | |
| Li et al. | Security and energy-aware collaborative task offloading in D2D communication | |
| CN113407977A (en) | Cross-chain extension method and system based on aggregated signature | |
| Fang et al. | Toward low-cost and stable blockchain networks | |
| CN115604765A (en) | Computing offload optimization method and device, electronic equipment and storage medium | |
| Olakanmi et al. | Trust-aware and incentive-based offloading scheme for secure multi-party computation in Internet of Things | |
| Marar et al. | Hybrid blockchain | |
| Xie et al. | A secure, service priority‐based incentive scheme for delay tolerant networks | |
| Almuseelem | Energy-efficient and security-aware task offloading for multi-tier edge-cloud computing systems | |
| CN114202422A (en) | Block chain fragmentation method, block chain system and cross-fragmentation transaction processing method | |
| Zhu et al. | ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices | |
| CN116703601B (en) | Data processing method, device, equipment and storage medium based on block chain network | |
| Liu et al. | A clusterized firewall framework for cloud computing | |
| Paavolainen et al. | Risks from spam attacks on blockchains for intemet-of-things devices | |
| Sharifian et al. | A New Approach to Orphan Blocks in the Nakamoto Consensus Blockchain | |
| CN110378692A (en) | Data safety processing method and device based on block chain | |
| CN117332871A (en) | Federal learning processing method based on blockchain and related equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |